
On Information Asymmetry in Competitive MultiAgent Reinforcement Learning: Convergence and Optimality
In this work, we study the system of interacting noncooperative two Ql...
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On the Verification and Computation of Strong Nash Equilibrium
Computing equilibria of games is a central task in computer science. A l...
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Learning Nash Equilibria in Monotone Games
We consider multiagent decision making where each agent's cost function...
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The Nash Equilibrium with Inertia in Population Games
In the traditional gametheoretic set up, where agents select actions an...
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Probably Approximately Correct Nash Equilibrium Learning
We consider a multiagent noncooperative game with agents' objective fun...
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Prophet Inequality with Competing Agents
We introduce a model of competing agents in a prophet setting, where rew...
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Asymptotic Behavior of Bayesian Learners with Misspecified Models
We consider an agent who represents uncertainty about her environment vi...
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Competitive Safety Analysis: Robust DecisionMaking in MultiAgent Systems
Much work in AI deals with the selection of proper actions in a given (known or unknown) environment. However, the way to select a proper action when facing other agents is quite unclear. Most work in AI adopts classical gametheoretic equilibrium analysis to predict agent behavior in such settings. This approach however does not provide us with any guarantee for the agent. In this paper we introduce competitive safety analysis. This approach bridges the gap between the desired normative AI approach, where a strategy should be selected in order to guarantee a desired payoff, and equilibrium analysis. We show that a safety level strategy is able to guarantee the value obtained in a Nash equilibrium, in several classical computer science settings. Then, we discuss the concept of competitive safety strategies, and illustrate its use in a decentralized load balancing setting, typical to network problems. In particular, we show that when we have many agents, it is possible to guarantee an expected payoff which is a factor of 8/9 of the payoff obtained in a Nash equilibrium. Our discussion of competitive safety analysis for decentralized load balancing is further developed to deal with many communication links and arbitrary speeds. Finally, we discuss the extension of the above concepts to Bayesian games, and illustrate their use in a basic auctions setup.
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